AI adoption has moved quickly from experimentation to everyday business use. Recent McKinsey research found that 88% of organizations now use AI regularly in at least one business function. But widespread adoption has not automatically translated into widespread business value: just 6% of organizations qualified as “high performers” capable of capturing significant value from their AI investments.
Other research points to a similar gap. A 2025 MIT study found that 95% of AI projects do not deliver a profitable return. While the studies use different methodologies and produce different estimates, they point to a common challenge: Many enterprises are investing in AI without a clear path to meaningful ROI.
The question then becomes whether organizations have the expertise to turn AI’s potential into business results.
That’s where independent talent can play an important role, especially when your enterprise doesn’t have the right AI skills in-house.
Enterprise leaders say AI expertise is difficult to hire
AI is driving demand for a wide range of specialized capabilities. Enterprises need technical talent in areas such as AI development and data science, but they also need people who can translate AI into business outcomes and guide implementation within their specific operating environment.
Those skills can be difficult to access through traditional full-time hiring, especially as organizations compete for a limited pool of experienced professionals.
Independent talent gives enterprises another way to access those capabilities.
In research conducted for our 16th annual State of Independence report, executives were asked which skill categories were harder to access through full-time employees than through independent talent. Applied AI skills ranked first, ahead of every other skill category measured.
- 40% of executives said applied AI expertise was harder to access through FTEs than through independent talent.
- AI development followed closely behind, cited by 38% of executives.
- Other hard-to-access skills included creative and design expertise (32%), data science (27%), and executive advisors or senior subject-matter experts (25%).
These findings suggest that enterprises are not simply looking for more people who understand AI. They need professionals who can apply AI to real business problems and help deliver measurable outcomes.
Applied AI may be the missing link in your workforce strategy
Knowing how AI works is one thing. Knowing how to use it well in a business is another.
A company can have access to powerful AI tools and still not know which problems they're best suited to solve, how to set up a solid implementation, or how to measure the results. That's often why AI initiatives stall: the focus stays on the technology instead of the business outcome the company is after.
Independent professionals can help close that gap.
Instead of building every capability in-house, a company can bring in an outside expert for a specific AI project or business challenge. That person can draw on experience from similar projects, stay current on emerging tools, and understand how to fit AI into the processes and workflows already in place.
This works especially well when a company needs specialized expertise but doesn't need a permanent full-time role or can't justify one.
Our research suggests enterprises already see the value. Applied AI expertise wasn't just one of several areas where independent talent had an edge. It was the skill category executives said was hardest to find through traditional full-time hiring.
Why AI fluency is more than a technical skill
As companies get more sophisticated with AI, the definition of AI expertise is expanding.
Technical skills still matter. Companies need people who understand AI governance, security and the technology behind AI applications. But technical knowledge alone may not create business value.
Companies also need people who can connect what AI can do to how the business actually works.
Take an AI professional who understands a company's industry, customers and operating processes. They're likely to be better at spotting high-value use cases than someone who comes at the problem from a purely technical angle.
The same goes for business and functional leaders. If they know how to work AI into an existing process, they can turn a promising tool into a practical solution.
Independent talent can be especially well suited for this task. Many independent professionals have worked across multiple companies, industries and projects. That exposure gives them a look at different approaches to AI adoption, and it can help them identify opportunities that are hard to see from inside a single organization.
How to turn AI investment into business value
Independent talent isn't a guaranteed fix for the AI ROI challenge. Outside expertise can't make up for weak implementation, or for an AI use case that doesn't solve a meaningful business problem.
But it can give companies access to scarce expertise when and where they need it.
That may matter most as companies move past broad experimentation and focus on specific use cases with measurable outcomes. An independent AI specialist or data expert can help a company evaluate potential applications, refine a use case, strengthen an implementation, and decide whether the benefits justify the investment.
The next phase of enterprise AI may depend less on how many companies adopt the technology and more on how well they apply it.
Access to the right expertise will be part of the answer. And as our State of Independence research shows, independent talent may give companies access to one of the hardest resources to find through traditional hiring: applied AI expertise.
For companies working to close the gap between AI adoption and AI ROI, that expertise could become an increasingly important part of their workforce strategy.
For more 2026 State of Independence research, check out our full report.
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